Let’s say you’re trying to do a difficult classification on a dataset that has had a lot of preprocessing/transformation, like fMRI brain data. There are a million reasons why things could be going wrong.

All successful analyses are alike, but every unsuccessful analysis is unsuccessful in its own way http://t.co/iDV4Hu8VaW

the brain doesn’t work the way you think, so you’re analysing the wrong brain regions or representing things in a different way

there’s signal there but it’s represented at a finer-grained resolution than you can measure.

But the most likely explanation is that you screwed up your preprocessing (mis-imported the data, mis-aligned the labels, mixed up the X-Y-Z dimensions etc).

If you can’t classify someone staring at a blank screen vs a screen with something on it, it’s probably something like this, since visual input is pretty much the strongest and most wide-spread signal in the brain – your whole posterior cortex lights up in response to high-salience images (like faces and places).

In the time I spent writing this, Abe had already figured out what I meant 🙂